Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces
(IRSs), or large intelligent surfaces (LISs), 1 have received significant attention for their …

Improving the efficiency of algorithms for fundamental computations can have a widespread
impact, as it can affect the overall speed of a large amount of computations. Matrix …

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that
improves over the behavior policy that collected the dataset, while at the same time …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …

Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-
learning (IQL) addresses this by training a Q-function using only dataset actions through a …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …